<![CDATA[The increase in fuel oil prices and electric vehicles has become an issue that continues to develop on Twitter. This study aims to analyze public sentiment regarding fuel oil price increases and Public Electric Vehicle Charging Stations (SPKLU) in Indonesia, comparing the Naive Bayes and IndoBERT methods in sentiment classification. The research method includes tweet data collection, data preprocessing, public sentiment labeling, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The preprocessing stage consists of case folding, tokenization, stopword removal, and stemming of Indonesian-language text. The data consist of 700 tweets collected from Twitter using the keywords related to fuel oil, electric vehicles, and Public Electric Vehicle Charging Stations (SPKLU). The results show that the IndoBERT method has better performance than Naive Bayes in sentiment classification because it is able to understand the context of the Indonesian language. This study is expected to contribute to the development of Indonesian-language sentiment analysis.]]>
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